Gage locations

USGS2 and DS2 have the same coordinates. USGS1 and DS3 have the same coordinates.

Hydrographs

Notes on gages: So far DS2 and DS3 have not been used. US1 and DS1 are mostly point measurements.

Streamflow differencing between each gage

DS1 minus US1 (uppermost)

Average DS1 minus US1 difference

## [1] 0.03090149
Year Avg streamflow difference (m3/s)
2011 0.0232853
2012 0.0138214
2013 0.0334710
2014 0.0497037
2015 0.0665000
2016 NaN
2017 NaN
2018 NaN
2019 NaN
2020 NaN

USGS2 minus DS1

Average USGS2 minus DS1 difference

## [1] 0.04210856
Year Avg streamflow difference (m3/s)
2011 0.0363444
2012 0.0106020
2013 0.0944612
2014 NaN
2015 NaN
2016 NaN
2017 NaN
2018 NaN
2019 NaN
2020 NaN

USGS1 minus USGS2

Average USGS1 minus USGS2 difference

## [1] -0.0001941755
Year Avg streamflow difference (m3/s)
2011 -0.0231274
2012 0.0108739
2013 0.0138716
2014 NaN
2015 NaN
2016 NaN
2017 NaN
2018 NaN
2019 NaN
2020 NaN

Sum of losses between USGS1 and USGS2

Year Month Monthly sum of losses (m3/s)
2011 4 -0.3270991
2011 5 -1.0456047
2011 6 -3.5999679
2011 7 0.0700276
2011 8 0.6986662
2011 9 -0.0434309
2012 4 1.9742787
2012 5 0.4860510
2012 6 0.1782581
2012 8 0.2018424
2012 9 -0.0854056
2013 5 2.4741706
2013 6 -0.5456931
2013 7 -0.3966977
2013 8 -0.5607375

Incorporating pumping

Precip

Compare Niwot SNOTEL precip to prism pixel

Original prism and Niwot SNOTEL precip comaprison

Prism shift 1 day and Niwot SNOTEL precip

Prism shift by fraction and Niwot SNOTEL precip

Compare prism solar rad to flux solar rad

Original Prism and Niwot flux tower solar rad

Prism shift by 1 day versus Niwot flux tower solar rad

Prism shift by fraction versus Niwot flux tower solar rad

Daymet (original) versus Niwot flux tower solar rad

Solar rad gap filling

Runoff ratios

prec_runoff <- read_csv('C:/Users/sears/Documents/Repos/fourmile/data/prec_runoff.csv')

ratios_prep <- prec_runoff %>%
  mutate(prec_12 = hru5 + hru6,
         prec_13 = hru3 + hru11,
         prec_10 = hru1 + hru2 + hru8 + hru9 + hru4,
         prec_11 = hru7 + hru10,
         date = dmy(time)) %>%
  dplyr::select(c(date, prec_12, prec_13, prec_10, prec_11)) %>%
  mutate_at(c('prec_12', 'prec_13', 'prec_10', 'prec_11'), ~(./1000)) %>%
  mutate(prec_12_cmd = prec_12 * (14378900+16275100),
         prec_13_cmd = prec_13 * (6850100+4299900),
         prec_10_cmd = prec_10 * (4092400+2170400+2379900+1725600+1967700),
         prec_11_cmd = prec_11 * (7092000+5834400)) %>%
  dplyr::select(-c(prec_12, prec_13, prec_10, prec_11))


obs_q <- read_csv('C:/Users/sears/Documents/Repos/fourmile/data/orun.csv')

obs_q <- obs_q %>% 
  dplyr::select(-1) %>%
  filter(!row_number() %in% c(1:9, 11:15)) %>%
  set_names(as.character(slice(., 1))) %>%
  slice(-1) %>%
  replace(.==-9999, NA) %>%
  rename(ds3 = 2,
         usgs1 = 3,
         ds2 = 4,
         usgs2 = 5,
         us1 = 6,
         ds1 = 7) %>%
  mutate(date = mdy(date)) %>%
  mutate_if(is.character, as.numeric) %>%
  dplyr::select(-c(ds2, ds3)) %>%
  mutate(across(where(is.numeric), ~(.*86400))) %>%
  filter(!between(date, as.Date("2013-09-10"), as.Date("2014-04-01")))
  
ratios <- full_join(ratios_prep, obs_q, by = 'date')

summary(ratios)
##       date             prec_12_cmd       prec_13_cmd       prec_10_cmd     
##  Min.   :2011-01-01   Min.   :      0   Min.   :      0   Min.   :      0  
##  1st Qu.:2013-06-09   1st Qu.:      0   1st Qu.:      0   1st Qu.:      0  
##  Median :2015-11-16   Median :   1836   Median :      0   Median :      0  
##  Mean   :2015-11-16   Mean   :  92963   Mean   :  32610   Mean   :  91074  
##  3rd Qu.:2018-04-24   3rd Qu.:  62572   3rd Qu.:  15649   3rd Qu.:  42265  
##  Max.   :2020-10-01   Max.   :5526738   Max.   :2546768   Max.   :8107303  
##  NA's   :43           NA's   :44        NA's   :44        NA's   :44       
##   prec_11_cmd            usgs1            usgs2               us1        
##  Min.   :      0.0   Min.   :     0   Min.   :   486.8   Min.   :   864  
##  1st Qu.:      0.0   1st Qu.:  1186   1st Qu.:  3512.7   1st Qu.:  2938  
##  Median :    914.3   Median :  3314   Median :  9588.1   Median :  8597  
##  Mean   :  40929.1   Mean   : 17481   Mean   : 19677.2   Mean   : 20489  
##  3rd Qu.:  30861.9   3rd Qu.: 14759   3rd Qu.: 24589.9   3rd Qu.: 24192  
##  Max.   :2301362.4   Max.   :694818   Max.   :221020.5   Max.   :293587  
##  NA's   :44          NA's   :546      NA's   :3081       NA's   :3463    
##       ds1        
##  Min.   :   864  
##  1st Qu.:  2419  
##  Median : 10123  
##  Mean   : 23001  
##  3rd Qu.: 29376  
##  Max.   :318125  
##  NA's   :3459
ratios_wy <- ratios %>%
  mutate(wy = calcWaterYear(date)) %>%
  group_by(wy) %>%
  summarize_if(is.numeric, sum, na.rm=TRUE) %>%
  slice(-c(11:12)) %>%
  mutate(us1_rr = ifelse(is.na(us1), 
                          NA, us1 / prec_12_cmd),
         ds1_rr = ifelse(is.na(ds1),
                          NA, ds1 / prec_13_cmd),
         usgs2_rr = ifelse(is.na(usgs2), 
                            NA, usgs2 / prec_10_cmd),
         usgs1_rr = ifelse(is.na(usgs1),
                            NA, usgs1 / prec_11_cmd))

kable(ratios_wy) %>%
  kable_styling()
wy prec_12_cmd prec_13_cmd prec_10_cmd prec_11_cmd usgs1 usgs2 us1 ds1 us1_rr ds1_rr usgs2_rr usgs1_rr
2011 27342604 10218925 27838457 11851929 4164660 4531636 1080197.1 1283376.7 0.0395060 0.1255882 0.1627833 0.3513909
2012 28454729 10745382 29542227 13705614 1598041 1428847 233263.1 274247.5 0.0081977 0.0255224 0.0483662 0.1165975
2013 49402678 17797008 51739581 20596886 4755085 4350351 581017.1 627806.4 0.0117608 0.0352760 0.0840817 0.2308643
2014 35068774 12570023 34894899 14845227 6821680 0 441475.2 517910.4 0.0125888 0.0412020 0.0000000 0.4595201
2015 42283708 15079372 41979012 18008048 13273551 0 569548.8 650160.0 0.0134697 0.0431159 0.0000000 0.7370899
2016 30236488 10796870 29526701 14119850 5801479 0 3888.0 4579.2 0.0001286 0.0004241 0.0000000 0.4108740
2017 33154630 10695612 29803300 14938516 5651282 0 0.0 0.0 0.0000000 0.0000000 0.0000000 0.3783028
2018 24878789 8264304 23018301 11390395 3315117 0 0.0 0.0 0.0000000 0.0000000 0.0000000 0.2910449
2019 29482356 9207441 25848560 13050871 4278182 0 0.0 0.0 0.0000000 0.0000000 0.0000000 0.3278081
2020 30734909 10747941 30124906 13241031 3815151 0 0.0 0.0 0.0000000 0.0000000 0.0000000 0.2881309
ratios_2014 <- ratios %>%
  mutate(wy = calcWaterYear(date)) %>%
  filter(wy == 2014) %>%
  mutate(usgs1_rr = usgs1 / prec_11_cmd)